Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network

Area of Science:

• Medical Imaging Analysis
• Artificial Intelligence in Healthcare
• Computational Pathology

Background:

• Pneumonia is a major global health threat, causing millions of deaths annually, particularly in vulnerable populations like infants and the elderly.
• Current diagnostic methods for pneumonia often lack sufficient accuracy and efficiency, necessitating advancements in detection systems.
• Early detection of pneumonia is crucial, especially for at-risk individuals with pre-existing conditions or those requiring mechanical ventilation.

Purpose of the Study:

• To investigate the efficacy of deep learning models for accurate pneumonia detection from X-ray images.
• To compare the performance of various pre-trained Convolutional Neural Network (CNN) architectures and their ensembles for pneumonia classification.
• To identify the optimal deep learning model for enhancing the accuracy and efficiency of pneumonia diagnosis.

Main Methods:

• Chest X-ray images were preprocessed for transfer learning tasks.
• Pre-trained CNN models, including VGG16, Inception-v3, and ResNet50, were employed.
• Ensemble models combining CNN with Inception-V3, VGG-16, and ResNet50 were created and evaluated.
• Performance was assessed using standard metrics, Cohen's kappa, and Area Under the Curve (AUC).

Main Results:

• The Inception-V3 model integrated with CNN demonstrated superior performance.
• This combined model achieved the highest accuracy at 99.29% and recall score of 99.73%.
• Ensemble models and other individual CNN variants also showed promising results in pneumonia detection.

Conclusions:

• Deep learning, particularly the Inception-V3 with CNN approach, offers a highly accurate method for pneumonia detection using X-ray images.
• The developed models show potential for improving early diagnosis and patient outcomes.
• Further research into AI-driven diagnostic tools is warranted to address limitations in current pneumonia detection systems.